Experimental perturbation of populations

As we noted in the introduction to this chapter, correlations can be suggestive, but a much more powerful test of the importance of a particular factor is to manipulate that factor and monitor the population's response. Predators, competitors or food can be added or removed, and if they are important in determining abundance, this should be apparent in subsequent comparisons of control and manipulated populations. Examples are discussed below, when we examine what may drive the regular cycles of abundance exhibited by some species, but we should note straight away that field-scale experiments require major investments in time and effort (and money), and a clear distinction between controls and experimental treatments is inevitably much more difficult to achieve than in the laboratory or greenhouse.

One context in which predators have been added to a population is when biological control agents (natural enemies of a pest - see Section 15.2.5) have been released in attempts to control pests. However, because the motivation has been practical rather than intellectual, perfect experimental design has not usually been a priority. There have, for example, been many occasions when aquatic plants have undergone massive population explosions after their introduction to new habitats, creating significant economic problems by blocking navigation channels and irrigation pumps and upsetting local fisheries. The population explosions occur as the plants grow clonally, break up into fragments and become dispersed. The aquatic fern, Salvinia molesta, for instance, which originated in southeastern Brazil, has appeared since 1930 in various tropical and subtropical regions. It was first recorded in Australia in 1952 and spread very rapidly - under optimal conditions Salvinia has a doubling time of 2.5 days.

Significant pests and parasites appear to have been absent. In 1978, Lake Moon Darra (northern Queensland) carried an infestation of 50,000 tonnes fresh weight of Salvinia covering an area of 400 ha (Figure 14.8).

Amongst the possible control agents collected from Salvinia's native range in Brazil, the black long-snouted weevil (Cyrtobagous sp.) was known to feed only on Salvinia. On June 3, 1980, 1500 adults were released in cages at an inlet to the lake and a further release was made on January 20, 1981. The weevil was free of any parasites or predators that might reduce its density and, by April 1981, Salvinia throughout the lake had become dark brown. Samples of the dying weed contained around 70 adult weevils per square meter, suggesting a total population of 1000 million beetles on the lake. By August 1981, there was estimated to be less than 1 tonne of Salvinia left on the lake (Room et al., 1981). This has been the most rapid success of any attempted biological control of one organism by the introduction of another, and it establishes the importance of the weevil in the persistently low abundance of Salvinia both after the weevil's introduction to Australia and in its native environment. It was a controlled experiment to the extent that other lakes continued to bear large populations of Salvinia.

Both the power and the problems of field-scale experiments are further illustrated by an example we have already discussed in Section 12.7.2, in which a 'predator' (in this case a parasite) was not added but removed. When Hudson et al. (1998) treated cyclic populations of the red grouse Lagopus lagopus against the nematode Trichostrongylus tenuis, the extent of the grouse 'crash' was very substantially reduced, proving the importance of the nematodes, normally, in reducing grouse abundance, and justifying the effort that had gone into the manipulation. But as we have seen, in spite of this effort, controversy remained about whether the nematodes had been proved to be the cause of the cycles (in which case, the residual smaller crashes were dying echoes) or whether, instead, the experiment had only proved a role for the nematodes in determining a cycle's amplitude, leaving their role in cyclicity itself uncertain. Experiments are better than correlations, but when they involve ecological systems in the field, eliminating ambiguity can never be guaranteed.

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